OpenRaiser/Intern-Atlas huggingface dataset:
Haptic Empathy: Investigating Individual Differences in Affective Haptic Communications
Venue: arXiv cs.HC 2025
Year: 2025

Nowadays, touch remains essential for emotional conveyance and interpersonal communication as more interactions are mediated remotely. While many studies have discussed the effectiveness of using haptics to communicate emotions, incorporating affect into haptic design still faces challenges due to individual user tactile acuity and preferences. We assessed the conveying of emotions using a two-channel haptic display, emphasizing individual differences. First, 24 participants generated 187 haptic messages reflecting their immediate sentiments after watching 8 emotionally charged film clips. Afterwards, 19 participants were asked to identify emotions from haptic messages designed by themselves and others, yielding 593 samples. Our findings suggest potential links between haptic message decoding ability and emotional traits, particularly Emotional Competence (EC) and Affect Intensity Measure (AIM). Additionally, qualitative analysis revealed three strategies participants used to create touch messages: perceptive, empathetic, and metaphorical expression.
On $G^p$-unimodality of radius functions in graphs: structure and algorithms
Venue: arXiv cs.DS 2025
Year: 2025

For every weight assignment $π$ to the vertices in a graph $G$, the radius function $r_π$ maps every vertex of $G$ to its largest weighted distance to the other vertices. The center problem asks to find a center, i.e., a vertex of $G$ that minimizes $r_π$. We here study some local properties of radius functions in graphs, and their algorithmic implications; our work is inspired by the nice property that in Euclidean spaces every local minimum of every radius function $r_π$ is a center. We study a discrete analogue of this property for graphs, which we name $G^p$-unimodality: specifically, every vertex that minimizes the radius function in its ball of radius $p$ must be a central vertex. While it has long been known since Dragan (1989) that graphs with $G$-unimodal radius functions $r_π$ are exactly the Helly graphs, the class of graphs with $G^2$-unimodal radius functions has not been studied insofar. We prove the latter class to be much larger than the Helly graphs, since it also comprises (weakly) bridged graphs, graphs with convex balls, and bipartite Helly graphs. Recently, using the $G$-unimodality of radius functions $r_π$, a randomized $\widetilde{\mathcal{O}}(\sqrt{n}m)$-time local search algorithm for the center problem on Helly graphs was proposed by Ducoffe (2023). Assuming the Hitting Set Conjecture (Abboud et al., 2016), we prove that a similar result for the class of graphs with $G^2$-unimodal radius functions is unlikely. However, we design local search algorithms (randomized or deterministic) for the center problem on many of its important subclasses.
Wild SBOMs: a Large-scale Dataset of Software Bills of Materials from Public Code
Venue: arXiv cs.SE 2025
Year: 2025

Developers gain productivity by reusing readily available Free and Open Source Software (FOSS) components. Such practices also bring some difficulties, such as managing licensing, components and related security. One approach to handle those difficulties is to use Software Bill of Materials (SBOMs). While there have been studies on the readiness of practitioners to embrace SBOMs and on the SBOM tools ecosystem, a large scale study on SBOM practices based on SBOM files produced in the wild is still lacking. A starting point for such a study is a large dataset of SBOM files found in the wild. We introduce such a dataset, consisting of over 78 thousand unique SBOM files, deduplicated from those found in over 94 million repositories. We include metadata that contains the standard and format used, quality score generated by the tool sbomqs, number of revisions, filenames and provenance information. Finally, we give suggestions and examples of research that could bring new insights on assessing and improving SBOM real practices.
xMOD: Cross-Modal Distillation for 2D/3D Multi-Object Discovery from 2D motion
Venue: arXiv cs.CV 2025
Year: 2025

Object discovery, which refers to the task of localizing objects without human annotations, has gained significant attention in 2D image analysis. However, despite this growing interest, it remains under-explored in 3D data, where approaches rely exclusively on 3D motion, despite its several challenges. In this paper, we present a novel framework that leverages advances in 2D object discovery which are based on 2D motion to exploit the advantages of such motion cues being more flexible and generalizable and to bridge the gap between 2D and 3D modalities. Our primary contributions are twofold: (i) we introduce DIOD-3D, the first baseline for multi-object discovery in 3D data using 2D motion, incorporating scene completion as an auxiliary task to enable dense object localization from sparse input data; (ii) we develop xMOD, a cross-modal training framework that integrates 2D and 3D data while always using 2D motion cues. xMOD employs a teacher-student training paradigm across the two modalities to mitigate confirmation bias by leveraging the domain gap. During inference, the model supports both RGB-only and point cloud-only inputs. Additionally, we propose a late-fusion technique tailored to our pipeline that further enhances performance when both modalities are available at inference. We evaluate our approach extensively on synthetic (TRIP-PD) and challenging real-world datasets (KITTI and Waymo). Notably, our approach yields a substantial performance improvement compared with the 2D object discovery state-of-the-art on all datasets with gains ranging from +8.7 to +15.1 in F1@50 score. The code is available at https://github.com/CEA-LIST/xMOD
Communication Access Real-Time Translation Through Collaborative Correction of Automatic Speech Recognition
Venue: arXiv cs.HC 2025
Year: 2025

Communication access real-time translation (CART) is an essential accessibility service for d/Deaf and hard of hearing (DHH) individuals, but the cost and scarcity of trained personnel limit its availability. While Automatic Speech Recognition (ASR) offers a cheap and scalable alternative, transcription errors can lead to serious accessibility issues. Real-time correction of ASR by non-professionals presents an under-explored CART workflow that addresses these limitations. We conducted a user study with 75 participants to evaluate the feasibility and efficiency of this workflow. Complementary, we held focus groups with 25 DHH individuals to identify acceptable accuracy levels and factors affecting the accessibility of real-time captioning. Results suggest that collaborative editing can improve transcription accuracy to the extent that DHH users rate it positively regarding understandability. Focus groups also showed that human effort to improve captioning is highly valued, supporting a semi-automated approach as an alternative to stand-alone ASR and traditional CART services.
Global Group Fairness in Federated Learning via Function Tracking
Venue: arXiv cs.LG 2025
Year: 2025

We investigate group fairness regularizers in federated learning, aiming to train a globally fair model in a distributed setting. Ensuring global fairness in distributed training presents unique challenges, as fairness regularizers typically involve probability metrics between distributions across all clients and are not naturally separable by client. To address this, we introduce a function-tracking scheme for the global fairness regularizer based on a Maximum Mean Discrepancy (MMD), which incurs a small communication overhead. This scheme seamlessly integrates into most federated learning algorithms while preserving rigorous convergence guarantees, as demonstrated in the context of FedAvg. Additionally, when enforcing differential privacy, the kernel-based MMD regularization enables straightforward analysis through a change of kernel, leveraging an intuitive interpretation of kernel convolution. Numerical experiments confirm our theoretical insights.
Machine Unlearning in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERU
Venue: arXiv cs.CV 2025
Year: 2025

Machine unlearning methods have become increasingly important for selective concept removal in large pre-trained models. While recent work has explored unlearning in Euclidean contrastive vision-language models, the effectiveness of concept removal in hyperbolic spaces remains unexplored. This paper investigates machine unlearning in hyperbolic contrastive learning by adapting Alignment Calibration to MERU, a model that embeds images and text in hyperbolic space to better capture semantic hierarchies. Through systematic experiments and ablation studies, we demonstrate that hyperbolic geometry offers distinct advantages for concept removal, achieving near perfect forgetting with reasonable performance on retained concepts, particularly when scaling to multiple concept removal. Our approach introduces hyperbolic-specific components including entailment calibration and norm regularization that leverage the unique properties of hyperbolic space. Comparative analysis with Euclidean models reveals fundamental differences in unlearning dynamics, with hyperbolic unlearning reorganizing the semantic hierarchy while Euclidean approaches merely disconnect cross-modal associations. These findings not only advance machine unlearning techniques but also provide insights into the geometric properties that influence concept representation and removal in multimodal models. Source code available at https://github.com/alex-pv01/HAC
Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems
Venue: arXiv cs.LG 2025
Year: 2025

Multi-Agent Deep Reinforcement Learning (MADRL) has emerged as a powerful tool for optimizing decentralized decision-making systems in complex settings, such as Dynamic Spectrum Access (DSA). However, deploying deep learning models on resource-constrained edge devices remains challenging due to their high computational cost. To address this challenge, in this paper, we present a novel sparse recurrent MARL framework integrating gradual neural network pruning into the independent actor global critic paradigm. Additionally, we introduce a harmonic annealing sparsity scheduler, which achieves comparable, and in certain cases superior, performance to standard linear and polynomial pruning schedulers at large sparsities. Our experimental investigation demonstrates that the proposed DSA framework can discover superior policies, under diverse training conditions, outperforming conventional DSA, MADRL baselines, and state-of-the-art pruning techniques.
Role-Selection Game in Block Production under Proposer-Builder Separation
Venue: arXiv cs.GT 2025
Year: 2025

To address the risks of validator centralization, Proposer-Builder Separation (PBS) was introduced in Ethereum to divide the roles of block building and block proposing, fostering a more equitable and decentralized block production environment. PBS creates a two-sided market in which searchers submit valuable bundles to builders for inclusion in blocks, while builders compete in auctions for block proposals. In this paper, we formulate and analyze a role-selection game that models how profit-seeking participants in PBS strategically choose between acting as searchers or builders, using a co-evolutionary framework to capture the complex interactions and payoff dynamics in this market. Through agent-based simulations, we demonstrate that agents' optimal role-acting as searcher or builder-responds dynamically to the probability of conflict between bundles. Our empirical game-theoretic analysis quantifies the equilibrium frequencies of role selection under different market conditions, revealing that low conflict probabilities lead to equilibria dominated by searchers, while higher probabilities shift equilibrium toward builders. Additionally, bundle conflicts have non-monotonic effects on agent payoffs and strategy evolution. Our results advance the understanding of decentralized block building and provide guidance for designing fairer and more robust block production mechanisms in blockchain systems.
BigO(Bench) -- Can LLMs Generate Code with Controlled Time and Space Complexity?
Venue: arXiv cs.CL 2025
Year: 2025

We introduce BigO(Bench), a novel coding benchmark designed to evaluate the capabilities of generative language models in understanding and generating code with specified time and space complexities. This benchmark addresses the gap in current evaluations that often overlook the ability of models to comprehend and produce code constrained by computational complexity. BigO(Bench) includes tooling to infer the algorithmic complexity of any Python function from profiling measurements, including human- or LLM-generated solutions. BigO(Bench) also includes of set of 3,105 coding problems and 1,190,250 solutions from Code Contests annotated with inferred (synthetic) time and space complexity labels from the complexity framework, as well as corresponding runtime and memory footprint values for a large set of input sizes. We present results from evaluating multiple state-of-the-art language models on this benchmark, highlighting their strengths and weaknesses in handling complexity requirements. In particular, token-space reasoning models are unrivaled in code generation but not in complexity understanding, hinting that they may not generalize well to tasks for which no reward was given at training time.
TruthLens:A Training-Free Paradigm for DeepFake Detection
Venue: arXiv cs.CV 2025
Year: 2025

The proliferation of synthetic images generated by advanced AI models poses significant challenges in identifying and understanding manipulated visual content. Current fake image detection methods predominantly rely on binary classification models that focus on accuracy while often neglecting interpretability, leaving users without clear insights into why an image is deemed real or fake. To bridge this gap, we introduce TruthLens, a novel training-free framework that reimagines deepfake detection as a visual question-answering (VQA) task. TruthLens utilizes state-of-the-art large vision-language models (LVLMs) to observe and describe visual artifacts and combines this with the reasoning capabilities of large language models (LLMs) like GPT-4 to analyze and aggregate evidence into informed decisions. By adopting a multimodal approach, TruthLens seamlessly integrates visual and semantic reasoning to not only classify images as real or fake but also provide interpretable explanations for its decisions. This transparency enhances trust and provides valuable insights into the artifacts that signal synthetic content. Extensive evaluations demonstrate that TruthLens outperforms conventional methods, achieving high accuracy on challenging datasets while maintaining a strong emphasis on explainability. By reframing deepfake detection as a reasoning-driven process, TruthLens establishes a new paradigm in combating synthetic media, combining cutting-edge performance with interpretability to address the growing threats of visual disinformation.
ChonkyBFT: Consensus Protocol of ZKsync
Venue: arXiv cs.DC 2025
Year: 2025

We present ChonkyBFT, a partially-synchronous Byzantine fault-tolerant (BFT) consensus protocol used in the ZKsync system. The proposed protocol is a hybrid protocol inspired by FAB Paxos, Fast-HotStuff, and HotStuff-2. It is a committee-based protocol with only one round of voting, single slot finality, quadratic communication, and n >= 5f + 1 fault tolerance. This design enables its effective application within the context of the ZKsync rollup, achieving its most critical goals: simplicity, low transaction latency, and reduced system complexity. The target audience for this paper is the ZKsync community and others worldwide who seek assurance in the safety and security of the ZKsync protocols. The described consensus protocol has been implemented, analyzed, and tested using formal methods.
Cube: A Roblox View of 3D Intelligence
Venue: arXiv cs.CV 2025
Year: 2025

Foundation models trained on vast amounts of data have demonstrated remarkable reasoning and generation capabilities in the domains of text, images, audio and video. Our goal at Roblox is to build such a foundation model for 3D intelligence, a model that can support developers in producing all aspects of a Roblox experience, from generating 3D objects and scenes to rigging characters for animation to producing programmatic scripts describing object behaviors. We discuss three key design requirements for such a 3D foundation model and then present our first step towards building such a model. We expect that 3D geometric shapes will be a core data type and describe our solution for 3D shape tokenizer. We show how our tokenization scheme can be used in applications for text-to-shape generation, shape-to-text generation and text-to-scene generation. We demonstrate how these applications can collaborate with existing large language models (LLMs) to perform scene analysis and reasoning. We conclude with a discussion outlining our path to building a fully unified foundation model for 3D intelligence.
The Impact of Big Five Personality Traits on AI Agent Decision-Making in Public Spaces: A Social Simulation Study
Venue: arXiv cs.HC 2025
Year: 2025

This study investigates how the Big Five personality traits influence decision-making processes in AI agents within public spaces. Using AgentVerse framework and GPT-3.5-turbo, we simulated interactions among 10 AI agents, each embodying different dimensions of the Big Five personality traits, in a classroom environment responding to misinformation. The experiment assessed both public expressions ([Speak]) and private thoughts ([Think]) of agents, revealing significant correlations between personality traits and decision-making patterns. Results demonstrate that Openness to Experience had the strongest impact on information acceptance, with curious agents showing high acceptance rates and cautious agents displaying strong skepticism. Extraversion and Conscientiousness also showed notable influence on decision-making, while Neuroticism and Agreeableness exhibited more balanced responses. Additionally, we observed significant discrepancies between public expressions and private thoughts, particularly in agents with friendly and extroverted personalities, suggesting that social context influences decision-making behavior. Our findings contribute to understanding how personality traits shape AI agent behavior in social settings and have implications for developing more nuanced and context-aware AI systems.
Approach to Visual Attractiveness of Event Space Through Data-Driven Environment and Spatial Perception
Venue: arXiv cs.HC 2025
Year: 2025

Revitalizing Japan's remote areas has become a crucial task, and Matsue City exemplifies this effort in its temporary event spaces, created through collective efforts to foster urban vibrancy and bring together residents and visitors. This research examines the relationship between data-driven in-sights using generative AI and visual attractiveness by evaluating tempo-rary events in Matsue City, particularly considering the cognitive-cultural differences in processing visual information of the participants. The first phase employs semantic keyword extraction from interviews, categorizing responses into physical elements, activities, and atmosphere. The second phase analyzes spatial perception through three categories: layout hierar-chy, product visibility, and visual attention. The correlation indicates that successful event design requires a balance between spatial efficiency and diverse needs, with a spatial organization that optimizes visitor flow and visibility strategies considering cultural and demographic diversity. These findings contribute to understanding the urban quality of temporary event spaces and offer a replicable framework for enhancing the visual appeal of events in remote areas throughout Japan.
Sensitivity to Redirected Walking Considering Gaze, Posture, and Luminance
Venue: arXiv cs.HC 2025
Year: 2025

We study the correlations between redirected walking (RDW) rotation gains and patterns in users' posture and gaze data during locomotion in virtual reality (VR). To do this, we conducted a psychophysical experiment to measure users' sensitivity to RDW rotation gains and collect gaze and posture data during the experiment. Using multilevel modeling, we studied how different factors of the VR system and user affected their physiological signals. In particular, we studied the effects of redirection gain, trial duration, trial number (i.e., time spent in VR), and participant gender on postural sway, gaze velocity (a proxy for gaze stability), and saccade and blink rate. Our results showed that, in general, physiological signals were significantly positively correlated with the strength of redirection gain, the duration of trials, and the trial number. Gaze velocity was negatively correlated with trial duration. Additionally, we measured users' sensitivity to rotation gains in well-lit (photopic) and dimly-lit (mesopic) virtual lighting conditions. Results showed that there were no significant differences in RDW detection thresholds between the photopic and mesopic luminance conditions.
Analysis of AI Effectiveness in Reducing Human Errors in Processing Transportation Requests
Venue: arXiv cs.HC 2025
Year: 2025

This article examines the characteristics of human errors in processing transportation requests. The role of artificial intelligence (AI) in maritime transportation is explored. The main methods and technologies used for automating and optimizing the handling of transportation requests are analyzed, along with their impact on reducing the number of errors. Examples of successful AI implementation in large companies are provided, confirming the positive influence of these technologies on overall operational efficiency and customer service levels.
Can AI Assist in Olympiad Coding
Venue: arXiv cs.SE 2025
Year: 2025

As artificial intelligence programs have become more powerful, their capacity for problem-solving continues to increase, approaching top-level competitors in many olympiads. Continued development of models and benchmarks is important but not the focus of this paper. While further development of these models and benchmarks remains critical, the focus of this paper is different: we investigate how AI can assist human competitors in high-level coding contests. In our proposed workflow, a human expert outlines an algorithm and subsequently relies on an AI agent for the implementation details. We examine whether such human-AI collaboration can streamline the problem-solving process and improve efficiency, highlighting the unique challenges and opportunities of integrating AI into competitive programming contexts.